code: def test_jax_model_for_regression():
      tasks, dataset, transformers, metric = get_dataset(
          "regression", featurizer="ECFP"
      )
  
      # sample network
      def forward_model(x):
          net = hk.nets.MLP([512, 256, 128, 2])
          return net(x)
  
      def rms_loss(pred, tar, w):
          return jnp.mean(optax.l2_loss(pred, tar))
  
      # Model Initialization
      params_init, forward_fn = hk.transform(forward_model)
      rng = jax.random.PRNGKey(500)
      inputs, _, _, _ = next(iter(dataset.iterbatches(batch_size=256)))
      modified_inputs = jnp.array(
          [
              x.astype(np.float32) if x.dtype == np.float64 else x
              for x in inputs
          ]
      )
      params = params_init(rng, modified_inputs)
  
      # Loss Function
      criterion = rms_loss
  
      # JaxModel Working
      j_m = JaxModel(
          forward_fn,
          params,
          criterion,
          batch_size=256,
          learning_rate=0.001,
          log_frequency=2,
      )
      _ = j_m.fit(dataset, nb_epochs=25, deterministic=True)
      scores = j_m.evaluate(dataset, [metric])
      assert scores[metric.name] < 0.5
  
file_path: deepchem/deepchem/models/jax_models/tests/test_jax_model.py
imports: import pytest
  from deepchem.models.tests.test_graph_models import get_dataset
  import deepchem
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: test_jax_model_for_regression() and returns: net, jnp